Artificial intelligence has no shortage of attention in financial services. Across banks, fintechs, regulators and technology companies, organisations are experimenting with new tools, testing use cases and exploring what AI could mean for their businesses.

But experimentation is only the beginning.

In a recent episode of the UKAI Women in AI Podcast, Tram Anh Nguyen, Co-Founder of CFTE and Chair of Global Women in AI, discussed a more difficult question: what does it take to turn AI experimentation into genuine, responsible adoption?

Her argument is that the conversation now needs to move beyond whether an organisation is using AI.

Instead, financial institutions need a clearer understanding of where AI is changing work, what value it creates, who remains accountable and what controls are required.

Moving beyond the pilot

AI pilots play an important role. They allow teams to explore capabilities, test assumptions and learn how emerging technologies behave in real environments.

The challenge comes when organisations try to move from an interesting experiment to something that can be embedded into day-to-day work.

At that point, the questions change.

A use case needs to be understood in the context of a workflow. Its value needs to be identifiable. Responsibility needs to remain clear. Risks and controls need to be considered. And the people working with the technology need enough understanding to exercise judgement around it.

This is why AI adoption is as much a capability challenge as it is a technology challenge.

When Tram Anh co-founded CFTE in 2017, the organisation was built around a structural problem she had observed in financial services: technology was changing the sector faster than professionals, institutions and regulators could learn. The gap was not necessarily a lack of ambition, but access to relevant, practical and industry-led education.

Generative AI and agentic AI have made that challenge even more important.

Financial services needs a shared language for AI adoption

One of the themes raised in the podcast is the amount of AI activity already taking place across financial services.

Banks are exploring use cases. Startups are building new products. Consulting firms and technology providers are publishing research. Regulators are examining the implications. Individual teams are testing tools in different parts of their organisations.

That creates a considerable amount of knowledge.

The harder task is making that knowledge useful across the industry.

In the conversation, Tram Anh describes the need for a shared language for AI adoption in finance, built around practical questions such as:

• Where is AI being applied?
• Which workflows are changing?
• What value is being created?
• Who is accountable?
• What controls are being considered?
• What can institutions learn from use cases already being developed elsewhere?

This is also part of the thinking behind CFTE’s work with the Financial Conduct Authority on initiatives that translate live AI experimentation into structured insight and shared learning.

The aim is not to suggest that there is a single model for adopting AI. Different institutions have different customers, systems, risk profiles, responsibilities and operating environments.

But greater visibility into real use cases can help organisations move from abstract discussions about AI towards more informed conversations about its practical application.

As Tram Anh explains, structured use cases can help organisations see who is doing what and create a body of knowledge that can evolve as the industry develops.

AI capability cannot sit with the technology team alone

There is another implication.

If AI increasingly affects workflows across an organisation, understanding AI cannot remain confined to data scientists, engineers or innovation teams.

Professionals in finance, legal, compliance, operations, risk, product and leadership already have something extremely valuable: domain expertise.

The opportunity is to combine that expertise with greater AI literacy and confidence.

Towards the end of the UKAI conversation, Tram Anh describes this as becoming a “supercharged professional”. The idea is not that every professional should become an AI technologist.

Someone may already have years of expertise in finance, law, security or another discipline. Rather than starting again, they can build on that foundation by understanding AI tools, applications and their implications for their field.

That distinction matters.

The future of AI in financial services will not only be determined by who can build a model. It will also depend on whether professionals with deep sector knowledge can ask the right questions, evaluate appropriate applications, understand limitations and contribute to decisions about how AI is incorporated into work.

Capability building is part of the infrastructure for adoption

Technology can move extraordinarily quickly. Organisational capability does not develop automatically at the same pace.

Building that capability requires more than awareness.

Professionals need opportunities to understand current applications, learn from real use cases, practise with emerging tools, exchange perspectives with peers and connect technological possibilities with the realities of their own work.

For financial institutions, this creates a broader question:

As AI capabilities accelerate, how do we ensure that the people responsible for applying, managing and governing them develop alongside the technology?

There will not be one answer for every organisation.

But the industry can learn faster when knowledge is shared, use cases are made visible and professionals have opportunities to develop the confidence to engage with AI rather than observe it from a distance.

From experimentation to informed adoption

AI in financial services is still evolving quickly.

That makes experimentation valuable. It also makes structured learning increasingly important.

The next phase is unlikely to be defined by the number of AI pilots an organisation can launch. More meaningful indicators will include whether useful applications can be connected to real workflows, whether value can be understood, whether accountability remains clear and whether appropriate controls are considered.

Above all, adoption will depend on people.

As Tram Anh puts it in the podcast, professionals should not assume that AI requires them to discard the expertise they have spent years developing. That expertise is the starting point.

The opportunity is to add AI capability to it.

Watch Tram Anh Nguyen’s full conversation on the UKAI Women in AI Podcast:
YouTube: https://www.youtube.com/watch?v=CVPBShDRRrs

Listen on Apple Podcasts:
https://podcasts.apple.com/us/podcast/why-ai-leadership-needs-more-women-with-tram-anh-nguyen-91/id1814947879?i=1000778028383

The post From AI Pilots to Responsible Adoption: What Financial Services Needs Next appeared first on CFTE.

The post What Do Payments Professionals Need to Know About Agentic Payments? appeared first on CFTE.

Singapore’s approach to AI is compelling because it treats AI not simply as a technology agenda, but as a workforce and inclusion agenda. Its ambition to develop 100,000 “AI Bilingual” workers shows what it can look like when technology adoption, professional skills, job transformation and lifelong learning move forward together.

There is something particularly impressive about witnessing Singapore’s approach to artificial intelligence.

The conversation is not only about building better models, attracting AI companies or accelerating technology adoption. It is increasingly about a much bigger question:

How do you prepare an entire workforce for a world in which AI changes the nature of work?

Our co-founder, Tram Anh Nguyen, recently had the opportunity to exchange perspectives with Joe Loy, Assistant Chief Executive and Managing Director, Digital Business & Corporate Solutions at NTUC LearningHub, and Patrick Tay, NTUC Assistant Secretary-General. The conversations explored the future of skills in an AI-powered world and, importantly, what it will take to make sure people are able to participate in that future rather than being left behind by it.

That distinction matters.

From AI adoption to AI bilingualism

Singapore is working towards supporting 100,000 workers to become “AI Bilingual” by 2029.

The idea behind AI bilingualism is powerful because it does not assume that everyone needs to become an AI engineer.

Instead, it combines two forms of expertise: a person’s knowledge of their own profession and the practical ability to understand and apply AI within that profession.

A lawyer remains a legal professional, but understands how AI can transform legal research and workflows.

An accountant retains deep financial and professional judgement, while learning where AI can improve analysis and productivity.

An HR professional understands people, organisations and talent, while becoming capable of applying AI responsibly to parts of their work.

Singapore’s National AI Impact Programme is beginning this effort with professions including accountancy and law, with plans to extend into areas such as HR. The TechSkills Accelerator programme is being expanded to help workers develop the practical AI capabilities needed to transform domain-specific workflows.

This changes the question from:

“How many people know how to use AI?”

to:

“How many people can combine AI with their professional expertise to do their work differently and better?”

That is a much more meaningful measure of workforce readiness.

Building the capability journey

A useful way to think about this transformation is as a progression:

AI awareness → AI literacy → AI fluency → AI application → AI specialisation

Awareness means understanding that AI is changing your industry.

Literacy means understanding what AI can and cannot do.

Fluency means being confident enough to work with AI within your profession.

Application means integrating AI into real workflows and using it to improve how work gets done.

Specialisation means developing deeper capabilities where the profession or role demands them.

The important point is that people do not need to jump immediately from having little experience with AI to becoming technical specialists.

There needs to be a pathway.

And there needs to be an infrastructure around that pathway that includes employers, education providers, government, professional bodies and workers themselves.

AI transformation is also job transformation

This is what makes the Singapore model particularly interesting.

It is not simply about teaching workers how to use the latest AI tool.

The broader challenge is about upskilling and reskilling at scale, redesigning jobs, helping enterprises transform workflows and giving workers the confidence to participate in an AI-enabled economy.

Singapore’s approach recognises that technology adoption and workforce transformation cannot happen independently.

If companies adopt AI without developing their people, the capability gap widens.

If people receive AI training but their jobs and organisational processes remain unchanged, much of that capability may never translate into meaningful impact.

And if access to AI skills is concentrated among a relatively small group of specialists, the economic benefits of the technology risk becoming concentrated too.

Singapore’s National AI Impact Programme therefore brings together both sides of the equation: it aims to support 100,000 workers in becoming AI Bilingual while helping 10,000 enterprises integrate AI into their business processes.

That connection between people and enterprises is critical.

AI should augment human potential

There is also an important principle behind this approach:

AI should augment human potential, not simply replace human potential.

This does not mean pretending that AI will not change jobs.

It will.

Tasks will change. Workflows will change. Some responsibilities will disappear while new ones emerge. The skills associated with many professions will evolve.

The question is whether workers have the opportunity to evolve alongside them.

Singapore’s current policy discussions explicitly recognise this challenge. Its tripartite partners have emphasised the need for job redesign and workforce reskilling as AI adoption accelerates, alongside practical support to help workers develop AI fluency.

This is where the conversation about lifelong learning becomes particularly important.

The importance of institutions such as NTUC LearningHub

A national AI strategy ultimately has to translate from policy into people’s everyday working lives.

That requires institutions capable of turning large ambitions into practical learning opportunities.

NTUC LearningHub’s current AI-Ready SG approach focuses on building practical AI skills for work, moving from AI literacy towards applied capability. It sits within a much broader lifelong learning ecosystem designed to help individuals continually develop skills as their careers and industries evolve.

Our exchanges with Joe Loy and Patrick Tay reinforced how important this layer of the ecosystem is.

Because the AI transition will not happen through technology policy alone.

It will happen when an accountant changes the way they analyse information.

When an HR professional redesigns a workflow.

When a manager becomes confident enough to identify where AI can create value.

When an SME can adopt AI without needing to build an AI research team.

And when workers at different stages of their careers know there is a pathway for them to continue learning.

The real test of a national AI strategy

The success of an AI strategy will naturally be measured through investment, adoption, innovation and productivity.

But there should be another measure too.

Can people participate?

Are workers equipped to adapt?

Can professionals combine their existing expertise with new AI capabilities?

Can SMEs access the skills required to transform?

Can people whose jobs are changing find credible pathways towards new opportunities?

And is the next generation entering the workforce prepared not simply to use AI, but to exercise judgement alongside it?

These questions move AI from being purely a technology conversation to becoming a question of economic participation.

That may ultimately be one of the most important aspects of Singapore’s approach.

The country has an opportunity to demonstrate what a genuinely inclusive national AI strategy can look like: one where technology, skills, enterprises, workers and opportunity move forward together.

It was impressive to witness these ideas being translated into action and to exchange perspectives with NTUC and NTUC LearningHub on what the future of skills could look like.

At CFTE, we look forward to continuing these conversations and advancing the work around building the capabilities people and organisations will need in an AI-powered world.

The post What Singapore Gets Right About Building an AI-Ready Workforce appeared first on CFTE.

Much of the innovation in payments over the past decade has focused on making it easier for people to pay.

Contactless cards reduced friction at the point of sale. Digital wallets changed how people interact with cards and accounts. Real-time payments accelerated the movement of money. Embedded finance made the payment itself less visible within the customer journey.

Agentic payments introduce a different question:

What happens when software starts making some of those decisions on our behalf?

Cloudflare’s move into wallets for AI agents is one example of this idea moving closer to real-world infrastructure.

An AI agent could potentially be given a task, a budget and a set of rules, then interact with different services and complete a transaction for its user.

That creates new possibilities. But it does not remove the fundamentals of payments.

The payment still has to work

An agent may decide what to buy or which provider to use, but the transaction still has to move through a payment system.

There still needs to be a payment method. The transaction needs to be authenticated, processed and settled. Fraud and compliance controls still apply.

There also needs to be clarity around who is authorised to spend, what limits apply and who is responsible if something goes wrong.

This is why agentic payments are not simply an AI topic. They are also a payments topic.

The infrastructure underneath the agent matters

When a new technology arrives, much of the attention naturally goes to what is new.

In this case, that is the agent.

But understanding what the agent changes requires an understanding of the infrastructure it is interacting with.

For payments professionals, that includes digital wallets, payment rails, real-time payments, settlement, fraud, regulation, compliance and newer developments such as stablecoins.

Without that context, it becomes difficult to separate what is genuinely new about agentic payments from what still depends on existing payments infrastructure.

This is one of the areas covered in CFTE’s Payments in Digital Finance Specialisation, which gives professionals a broader understanding of the payments ecosystem and the technologies reshaping it.

Explore Payments in Digital Finance

What does AI actually change?

The more significant change may happen around the decisions made before and during a payment.

An AI agent could potentially compare providers, choose between payment options, determine whether a transaction falls within an allocated budget, initiate a payment or respond when a transaction fails.

That introduces a different set of questions.

How much authority should an agent have?

Which decisions can safely be automated?

When should human approval still be required?

How do fraud controls need to adapt when more decisions are automated?

And what happens when an agent makes the wrong decision?

These questions sit at the intersection of AI and payments.

For payments professionals, the challenge is not simply understanding what AI agents can do. It is understanding how those capabilities interact with the infrastructure, controls and responsibilities that already sit behind a payment.

The post AI Agents Are Getting Wallets. What Does That Mean for Payments? appeared first on CFTE.

Why financial institutions need role-specific AI capability, not one-size-fits-all training

On 2 August 2026, the EU AI Act enters another major phase of application.

The EU AI Act is the European Union’s risk-based framework for governing how artificial intelligence is developed, provided and used. It applies to public and private organisations inside and outside the EU when they place AI systems on the EU market, introduce them into service or use them within the EU. This means the Act is relevant not only to technology companies developing AI, but also to organisations using it.

For financial institutions, this may include AI used in customer service, compliance, financial crime, risk management, credit assessment, investment analysis and employee productivity.

From August 2026, transparency requirements for certain AI systems and AI-generated content become applicable. National authorities will also begin supervising and enforcing the AI literacy obligation that has applied since February 2025. Although some deadlines for high-risk AI systems have recently been delayed, the August transparency and AI literacy enforcement milestones remain highly relevant to organisations using AI.

The question for financial institutions is therefore no longer simply whether they have an AI policy.

It is whether their people have the knowledge and professional judgement required to put that policy into practice.

AI literacy is already an organisational responsibility

Article 4 of the EU AI Act has applied since 2 February 2025.

It requires providers and deployers of AI systems to take measures to ensure that employees and others operating AI on their behalf have a sufficient level of AI literacy.

The appropriate level should reflect their technical knowledge, experience, education and training, as well as the context in which an AI system is used and the people it may affect.

This has an important practical implication:

AI literacy cannot be treated as a single introductory course delivered identically across an organisation.

A senior leader deciding where AI should be deployed needs different capabilities from a compliance professional assessing an AI-enabled process.

A product manager integrating AI into a customer journey has different responsibilities from an employee using generative AI to draft, research or analyse information.

They all require AI literacy, but they do not require the same AI literacy.

Why general AI awareness is not enough

General awareness training can create a useful shared foundation, but it may not equip people to carry out their specific responsibilities.

Knowing what generative AI is does not necessarily mean that an employee can recognise an unreliable output, protect confidential information or determine when human review is required.

Understanding basic AI concepts does not automatically enable a manager to assess whether an AI use case is appropriate for a regulated workflow.

Completing a general course may not equip a compliance professional to challenge a technology provider, identify governance gaps or assess whether human oversight is meaningful.

The European Commission states that there is no one-size-fits-all approach to AI literacy. Organisations should consider the systems they provide or deploy, the risks associated with them and the existing knowledge and responsibilities of the people interacting with them.

Without role-specific capability, organisations may experience:

• • Inconsistent or unreliable use of AI affecting business decisions

• • Weak human oversight, fragmented accountability and difficulty challenging technology providers

• • Exposure of sensitive information and increased regulatory and reputational risk

Regulation alone cannot create operational readiness. Organisations need people who can use, evaluate, challenge and oversee AI within the context of their responsibilities.

Build role-specific AI literacy with CFTE

Financial institutions need a shared foundation of AI knowledge, supported by deeper learning for professionals applying and governing AI.

CFTE provides three complementary pathways designed around different roles and responsibilities.

No individual course can guarantee compliance with the EU AI Act. Compliance depends on an organisation’s systems, risks, controls and governance. However, role-relevant learning can help build the knowledge and professional judgement needed to translate AI policies into practice.

Build essential foundations with AI Literacy for Professionals

AI Literacy for Professionals helps professionals understand, evaluate and confidently use AI while recognising its capabilities and limitations.

The programme develops foundational knowledge, critical evaluation of AI outputs, awareness of risks and greater confidence engaging with AI tools. It is suited to professionals building essential AI capabilities and organisations introducing workforce-wide AI literacy.

The outcome is not simply greater awareness. It is a workforce better equipped to make informed and responsible decisions about when and how AI should be used.

Apply generative AI with Generative AI 360 for Financial Services

Generative AI 360 for Financial Services helps professionals understand how generative AI is changing finance and how it can create practical value.

The programme combines expert-led learning with financial-services applications, helping business, product, operations, innovation and transformation professionals identify credible use cases and contribute more effectively to AI adoption.

Learners move beyond general awareness towards an applied understanding of how generative AI can support financial-services activities.

Strengthen governance with Generative AI for Compliance

Generative AI for Compliance in Financial Services is designed specifically for compliance professionals and other teams responsible for risk, governance and oversight.

Using compliance-focused applications and case studies, the programme explores regulatory reporting, risk identification, regulatory change and ethical AI practices. It helps professionals evaluate AI-enabled processes and engage more confidently with business and technology teams.

The outcome is stronger professional challenge and more informed oversight when AI is introduced into regulated processes.

One requirement, three complementary pathways

The EU AI Act creates an organisation-wide need for AI literacy, but the appropriate pathway depends on each person’s responsibilities.

AI Literacy for Professionals establishes the foundations required to understand, evaluate and use AI responsibly.

Generative AI 360 for Financial Services develops applied understanding of generative AI across finance.

Generative AI for Compliance in Financial Services builds specialist capabilities for compliance, risk and governance professionals.

Together, the programmes help institutions establish shared foundations, build practical capabilities and strengthen informed oversight.

Why CFTE?

CFTE develops applied AI capabilities specifically for financial-services professionals and institutions.

Its programmes combine AI knowledge with financial-services applications, professional responsibilities and practical implementation. CFTE’s AI in Finance Academy has supported the training of more than 20,000 professionals globally.

Prepare your workforce for responsible AI adoption

Preparing for the EU AI Act requires more than adding a general AI module to mandatory training.

Financial institutions need to understand who interacts with AI, which systems they use, what risks those systems create and what level of knowledge each group requires.

CFTE works with institutions to align AI learning pathways with workforce roles, organisational priorities and the context in which AI is being used.

Individual professionals can also select the pathway that best reflects their responsibilities:

• • Start with AI Literacy for Professionals to build essential knowledge and responsible-use capabilities.

• • Choose Generative AI 360 for Financial Services to understand and apply generative AI across finance.

• • Choose Generative AI for Compliance in Financial Services to strengthen compliance, risk, governance and oversight capabilities.

The question is no longer whether financial institutions will use AI.

It is whether their people are equipped to use, challenge and oversee it effectively.

Explore CFTE’s AI learning pathways or speak with CFTE about building role-specific AI literacy across your organisation

The post The EU AI Act’s August 2026 Milestone: Is Your Workforce AI Literate? appeared first on CFTE.

KEY TAKEAWAYS

• Global Women in AI officially launched its Hong Kong chapter on 9 July 2026 at LEAP East, Hong Kong Convention and Exhibition Centre.
• Only a small fraction of AI leadership positions globally are held by women. GWAI exists to change that permanently.
• The launch featured a recorded address from Chairwoman Tram Anh Nguyen and a panel of four distinguished women leaders from finance, fintech, and AI.
• GWAI is building toward 10,000 supercharged women AI leaders across 100+ countries through education, mentorship and community.

The Real Question Is Not Who Will Use AI.

It Is Who Will Shape It.

Artificial intelligence is transforming every industry.

From finance and healthcare to education, government and entrepreneurship, AI is changing how decisions are made, how businesses operate and how societies evolve.

But one important question remains:

Who will shape the future of AI?

Today, women remain significantly underrepresented in AI leadership and decision-making roles.

This is not only a diversity challenge. It is a leadership challenge.

If the people building and governing AI do not represent the world these systems serve, we risk creating technology that reflects only a limited set of perspectives.

As Tram Anh Nguyen, Chairwoman of Global Women in AI and Co-Founder of CFTE, highlights:

“The real question is not only who will use AI. The real question is who will shape the future of AI.”

Tram Anh Nguyen: A Call for Women to Lead AI

At the launch of the Hong Kong Chapter, Tram Anh Nguyen will share her vision for a future where women are not only participating in AI, but actively shaping its direction.

Through GWAI, the goal is clear:

To equip women with the knowledge, confidence and networks needed to become leaders, decision-makers and innovators in AI.

Why Hong Kong. Why Now.

Hong Kong is a strategic location for GWAI’s expansion.

As a global financial centre and a growing hub for technology and innovation, Hong Kong sits at the intersection of finance, entrepreneurship and AI transformation.

Launching the Hong Kong Chapter at LEAP East represents a commitment to ensuring that women across Asia have a voice in the global AI conversation.

The chapter will become a platform for:

• Learning and capability building
• Meaningful connections and mentorship
• Leadership opportunities across industries

Meet the Leader Behind GWAI Hong Kong

Jessica Liu
CEO and Co-Founder, Planto
GWAI Executive Committee & GWAI Ambassador and Lead, Hong Kong Chapter

The Hong Kong Chapter will be led by Jessica Liu, whose experience across entrepreneurship, fintech and innovation reflects the type of leadership GWAI seeks to cultivate.

Jessica’s role is to build a community where women across Hong Kong can connect, learn and contribute to shaping the future of AI.

Women Leaders Joining the Conversation

The launch event will feature a panel discussion bringing together inspiring women leaders from finance, fintech and AI.

The conversation will explore:

• How AI is reshaping industries
• What leadership looks like in the AI era
• How women can actively participate in building and governing AI systems

Panel speakers include:

Jessica Liu
CEO and Co-Founder, Planto
GWAI Executive Committee & GWAI Ambassador and Lead, Hong Kong Chapter
Moderator

Xiaoyan Cheyenne Shu
Ex Principal Investcorp Founder, AI Infrastructure Investment Company

Jessica Lam
Group Chief Strategy Officer, WeLab

Michele Fung
Head of APAC, Unlimit
Board Member, Fintech Association of Hong Kong

Building a Global Movement

Global Women In AI is more than a community.

It is a global movement focused on three pillars:

Education

Building strong foundations in AI, digital skills and systems thinking across every sector and career stage.

Mentorship

Creating access to role models, networks and communities that help women grow their confidence and leadership.

Leadership

Supporting women as decision-makers, founders, investors, policymakers and innovators shaping AI.

The ambition is to build a global network of 10,000 supercharged women AI leaders across 100+ countries.

Join the Hong Kong Chapter Launch

The future of AI will be shaped by those who step forward today.

Join Global Women in AI at LEAP East as we celebrate the launch of the Hong Kong Chapter and bring together leaders committed to building a more inclusive AI future.

9 July 2026
Hong Kong Convention and Exhibition Centre

https://luma.com/k9tr14sl

The post Global Women in AI Launches Hong Kong Chapter at LEAP East 2026 appeared first on CFTE.

Today, CFTE hosted a webinar with Dr David R. Hardoon, PhD, CFTE Senior Advisor, Data and AI leader, and former Chief Data Officer at the Monetary Authority of Singapore, on “Agentic AI in Financial Services: From Governance to Operationalisations.”

The session focused on one of the most important questions facing financial institutions today:

How do we move from responsible AI principles and governance frameworks to practical implementation, assurance and controls?

As generative AI and AI agents become more embedded in financial services workflows, the challenge is no longer only about understanding the technology. It is about building the right operating model for responsible adoption.

The problem is not a lack of governance

Financial services is not short of governance.

There are frameworks, policies, principles, model risk processes, regulatory expectations, internal controls and approval committees. Yet, when organisations try to deploy AI, especially agentic AI, governance is often experienced as a blocker rather than an enabler.

During the webinar, participants identified familiar barriers to agentic AI adoption: governance, security, lack of expertise, data quality, control and awareness.

Dr Hardoon’s point was not that governance is unnecessary. Quite the opposite.

The intended purpose of governance is to create safeguards, manage risk, build trust, improve transparency and support long-term value. The problem is that, in practice, governance can become red tape, box-ticking and a process that slows down deployment without necessarily improving outcomes.

That gap matters.

If governance already feels difficult in traditional AI, agentic AI increases the complexity. Instead of one model producing one output, organisations may now be dealing with multiple agents, tools, workflows, decisions, escalations and human interactions.

This is why governance cannot remain a one-time approval gate.

Agentic AI is about orchestration

A key theme from the session was that agentic AI should not be understood as “AI acting alone.”

Dr Hardoon described agentic AI as an environment of specialised agents, task decomposition, memory, orchestration and collaboration. Some agents may perceive, some may reason, some may act, and others may coordinate.

But in financial services, the most important point is this:

Agentic systems will not consist only of AI agents.

They will sit alongside human agents, rule-based systems, existing operational processes and business teams. A true agentic environment is therefore not only a technical architecture. It is an operating model.

This creates a governance challenge.

If institutions govern AI agents in one way, rule-based automation in another way and human decision-making in another way, they risk creating inconsistent controls across the same business process.

The governance question becomes less about whether something is “AI” and more about what role it plays in the business process, what risk it introduces, and how it should be monitored.

The double standard problem

One of the strongest examples from the webinar was the idea of double standards.

When an AI solution accesses files, analyses information and drafts a memo, cybersecurity, legal or compliance teams may raise concerns. Those concerns may be valid.

But the next question is: how is the same task done today?

If a human already accesses the same files, reviews the same information and drafts the same memo, then the issue is not simply that “AI creates risk.” The issue is whether the new process changes the existing risk profile.

Does it increase risk?
Does it reduce risk?
Does it create new controls?
Does it expose gaps in the way the existing process was already governed?

This is where financial institutions need a more mature approach. AI should not be treated as special simply because it is AI. It should be governed according to the role it plays, the risk it introduces and the outcome it is expected to produce.

From one-time approval to continuous governance

Traditional governance often assumes a deterministic process.

A system is reviewed, approved, deployed and then periodically checked. That works reasonably well when inputs and outputs are stable and predictable.

Agentic AI is different.

The system may adapt, interact with changing data, respond differently depending on context and operate across a wider workflow. In this environment, governance has to become continuous.

Dr Hardoon highlighted several dimensions institutions need to monitor.

Observability means being able to see and understand what the agent or fleet of agents is doing.

Controllability means having a way for a human, another agent or a rule-based system to intervene when needed.

Stability means ensuring the wider system can remain reliable if one agent fails or performs below expectation.

Robustness means testing whether the system can handle adversarial prompts, market shocks, noise, system issues or unexpected inputs.

Performance means checking whether the system is still delivering the intended business outcome.

This is a very different mindset from asking whether a model passed an approval gate once.

It is closer to running an operating environment.

Governance as a business function

One of the most practical takeaways from the session was the need to treat governance as a business function.

That means governance should not sit outside the development lifecycle as a separate approval step. It should be embedded into the process from the beginning.

A practical AI governance operating model might include intake, assessment, build and test, deployment approval, operate and monitor, and retire or decommission.

At the intake stage, the AI use case is submitted, reviewed and added to an inventory with clear ownership.

At the assessment stage, risk, compliance, privacy, cybersecurity and business impact are reviewed early.

During build and test, evidence, attestations and controls are collected during development, not after everything is built.

Deployment approval should then be based on the risk tier, business context and evidence gathered.

Once deployed, performance, drift, value, risk indicators and compliance need to be monitored continuously.

Finally, institutions also need to think about retirement and decommissioning, including end-of-life decisions, migration and replacement.

This is where governance becomes useful. It becomes part of execution, rather than a hurdle that appears at the end.

The human capital gap

The session also ended with an important reminder: AI is ultimately about people.

Dr Hardoon noted that HR is often missing from AI initiatives, despite the fact that AI directly affects skills, roles, capabilities, operating models and talent strategy.

This is particularly important for agentic AI.

If AI agents begin to work alongside human agents, organisations need to rethink more than technology. They need to rethink capability building, role design, accountability, workforce readiness and leadership.

AI governance cannot be separated from human governance.

The question is not only: “How do we govern AI?”

It is also: “How do we govern work in an environment where humans, AI systems and rule-based processes operate together?”

What financial institutions should do next

The lesson from the webinar is clear.

Financial institutions do not need more abstract AI governance. They need governance that can be operationalised.

That means moving from policies to processes.

It means moving from one-time approvals to continuous monitoring.

It means moving from AI-specific controls to unified governance across humans, rules and AI agents.

It means moving from experimentation to operating models.

And it means moving from technical adoption to capability building.

Agentic AI will make governance more important, not less.

But the institutions that succeed will be the ones that make governance practical enough to accelerate responsible adoption, rather than slow it down.

The post Agentic AI in Financial Services: Governance Has to Move From Policy to Operating Model appeared first on CFTE.

Reflections from CFTE’s conversation with Dr David R. Hardoon in Abu Dhabi

There is a familiar problem inside many organisations trying to use AI.

The business wants to move quickly.
The technology team wants to experiment.
The governance team wants to understand the risks.
Leadership wants confidence before anything is scaled.

Too often, these teams meet too late.

A use case is built. A pilot is tested. Then governance is asked to approve it. At that point, the conversation can feel like a stop sign: too risky, not clear enough, not ready, not aligned.

But governance should not be where innovation stops.

That was one of the key messages from CFTE’s recent conversation with Dr David R. Hardoon in Abu Dhabi: good governance should help organisations move forward, not hold them back.

The problem is not governance. It is when governance starts.

AI is now part of real work in financial services. It is being used in customer service, fraud detection, compliance, operations, software development, research, productivity, and decision support.

That means governance can no longer be treated as a final approval step.

If governance only comes in at the end, teams are left guessing what is acceptable. They may build something that cannot be used. They may move too slowly. Or worse, they may avoid the process altogether because it feels disconnected from the work.

The better approach is to bring governance in earlier.

Before a team starts building, they should already understand the basic questions:

What data can we use?
What risks are we comfortable with?
Where do we need human oversight?
What happens if the system makes a mistake?
Who is responsible?

These questions do not stop innovation. They make innovation usable.

From “no” to “how”

Dr Hardoon has been part of CFTE’s AI journey since 2018. Back then, the conversation around AI in finance was still mostly about machine learning, data, model governance, and responsible experimentation.

Today, the conversation has changed.

Generative AI has entered daily workflows. Agentic AI is raising new questions about autonomy and oversight. Regulators are looking closely at how institutions balance innovation with trust.

But one message has stayed consistent: governance should not be the department of “no”.

Its role should be to help teams understand how something can be done safely, where the boundaries are, and whether the organisation is ready to proceed.

That is a much more useful way to think about governance.

AI cannot be governed once and forgotten

One reason AI is difficult to govern is that it does not behave like a normal business process.

A traditional process can often be reviewed, approved, documented, and checked again later.

AI is different.

Models change.
Outputs vary.
Users behave differently.
New risks can appear after deployment.

This is especially true when AI systems produce different answers depending on prompts, context, data, or user behaviour.

So governance cannot be a one-time gate. It has to continue after launch.

That does not mean making every AI project slow or heavy. It means having practical ways to monitor what is happening, spot issues early, and know when someone needs to step in.

Done well, governance does not slow teams down. It prevents them from reaching the end of a project and discovering that what they built cannot actually be used.

Agentic AI makes this even more important

The conversation became especially interesting when it turned to agentic AI.

Agentic AI is often described as AI that can act more independently. But for organisations, the bigger issue is not just the AI agent itself. It is the system around it.

In a bank, an AI tool does not operate in isolation. It connects with policies, customer data, internal workflows, human teams, escalation rules, and compliance requirements.

Take a call centre as an example.

The question is not only: did the AI give the right answer?

The better questions are:

Was the answer based on the right information?
Did it follow the bank’s policy?
Did it know when to escalate to a human?
Can the organisation see what happened?
Who is accountable if something goes wrong?

This is why AI governance cannot only focus on the model. It has to look at the whole system.

Governance is also about people

Another important point from the discussion was that governance is not only about policies and controls.

It is also about capability.

Many organisations already have access to AI tools. That is not the main problem.

The bigger challenge is that different teams understand AI in very different ways.

Business teams want speed.
Technology teams want to test.
Risk teams need clarity.
Leaders need confidence.
Employees need to know what responsible use looks like.

When these groups do not share a common language, AI adoption becomes slow and fragmented.

This is why capability building is part of governance.

A policy only works if people know how to apply it. A framework only matters if teams can use it in real decisions. Responsible AI principles only matter if they change how people design, use, monitor, and challenge AI systems.

Why this matters in Abu Dhabi and the UAE

David’s visit to Abu Dhabi reflects a wider priority for CFTE: bringing global experts into the UAE and the Middle East to support the region’s ambitions in AI, finance, and future skills.

The region is not watching the AI conversation from the sidelines.

Governments, regulators, financial institutions, and enterprises are actively investing in AI as a driver of productivity, competitiveness, workforce transformation, and innovation.

But what makes the conversation especially important is the focus on people.

AI adoption is not only about infrastructure, models, or tools. It is about whether leaders, teams, and institutions have the skills and judgement to use AI well.

That is where global expertise needs to meet local ambition.

The right question is not simply: what is global best practice?

A better question is: what is globally informed, locally relevant, and useful in practice?

The human skill that matters most

One of the strongest ideas from the conversation was about human judgement.

As AI becomes more capable, people do not become less important. Their role changes.

People need to become better at asking questions, framing problems, recognising trade-offs, interpreting outputs, and taking responsibility for decisions.

David described cognitive skills as a muscle. That is a useful way to think about AI readiness.

If AI helps more with execution, then human value moves towards judgement.

What should we ask?
What should we trust?
What should we challenge?
What could go wrong?
What does a responsible decision look like?

These are the questions that will determine whether AI creates real value or introduces new risks.

The real work ahead

We are grateful to Dr David R. Hardoon for joining us in Abu Dhabi and for continuing to contribute to CFTE’s work.

His message is timely for financial services and for the region.

Governance and innovation should not sit on opposite sides of the table. Governance should help teams move with more clarity. Innovation should bring governance into the process earlier. Capability building should give people the confidence to make better decisions.

At CFTE, this is why we bring experts like David to the UAE and the wider Middle East.

The aim is not to add another conversation about AI trends. It is to support the people and institutions building the future of finance in practice.

AI governance is not the brake.

Done properly, it is how organisations learn to move faster, safer, and with greater confidence.

The post AI Governance Is Not the Brake. It Is the Way Forward. appeared first on CFTE.

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The UK’s AI objectives are changing. It is no longer only a question of ambition, innovation or adoption. It is now a question of implementation.

We are entering the age of agentic AI: systems that do not only assist, but act. They can plan, reason across steps, coordinate with other agents and operate inside the tools organisations already use. Generative AI changed how people create and analyse information. Agentic AI will change how work is organised, how decisions are made and how institutions deliver value.

For the UK, this creates a strategic opening. The country has world-class research, a strong technology ecosystem, sophisticated regulators, deep sector expertise and a government that has placed AI at the centre of national growth. But if we want AI to translate into productivity, trust and inclusive prosperity, the next phase cannot be only about tools, models or infrastructure. It must also be about people.

The UK does not only need AI adoption. It needs AI fluency.

Adoption means access to tools. Fluency means the capability to understand what AI can do, where its limits are, when to trust it, when to challenge it and how to apply it responsibly in context. It is the difference between using AI as a faster task-doer and knowing how to redesign work around intelligent systems.

This distinction matters because agentic AI is not just another software upgrade. When a system can act, leaders must ask new questions. What should be automated? Who authorises an action? Who is accountable for the outcome? How do we audit decisions made or supported by autonomous systems? When should humans intervene? What happens when an agent acts across multiple systems, suppliers or jurisdictions?

These are not purely technical questions. They are questions of operating model, governance, skills and trust.

The opportunity is already visible. In financial services, Lloyds Banking Group has announced an AI-powered financial assistant for millions of customers. In education, the UK government is supporting the development of AI tutoring tools that could help disadvantaged pupils. In public services, police forces are experimenting with digital agents to respond to non-emergency citizen queries and free human teams to focus on higher-value work.

The next frontier is agentic commerce, where agents search, compare, negotiate and buy on behalf of individuals and businesses. For companies, the question will shift from “How do we reach the customer?” to “How do we become discoverable, trusted and chosen by the agent acting for the customer?”

This is the scale of the change ahead. Agentic AI will begin to decouple cognitive work from headcount. Organisations will be able to increase output without increasing people in the same way. Some professionals will be outcompeted by systems that are faster, cheaper and always available. Others will be supercharged, using AI with their judgement, domain expertise and contextual understanding to reach a new level of performance.

The dividing line will not be the technology. It will be capability.

From our work with financial institutions, regulators and governments internationally, we see the same pattern repeatedly. Organisations rarely struggle because they lack access to AI tools. They struggle because their people do not yet have the fluency to redesign workflows, govern risks and create value responsibly.

This is why AI fluency must become a national priority.

If fluency remains concentrated among a small group of technical experts or already advantaged firms, AI will widen the gap between those who can benefit and those who are displaced. But if fluency is built deliberately across sectors, regions and communities, it can become a foundation for broader participation in the AI economy.

For the UK, this requires a human-capital infrastructure for AI.

First, the UK needs a shared understanding of AI fluency. Not everyone needs to become an engineer. But citizens, frontline workers, managers, executives, regulators and board members all need the ability to use, question and govern AI at the right level for their role. A common fluency framework would help organisations move beyond generic awareness and define the capabilities people actually need.

Second, fluency must be built around sectors and roles. AI in banking is not the same as AI in healthcare, education, legal services, manufacturing or public administration. The workflows, risks, accountability structures and professional norms are different. The future belongs to AI-bilingual professionals: bankers, clinicians, civil servants, teachers, regulators and entrepreneurs who understand both their field and how AI transforms it.

Third, capability must be embedded into responsible deployment. Organisations should not be assessed only on whether they use AI, but on whether they have the skills, governance, auditability and oversight to use it well. Procurement, regulation and institutional strategy should reward responsible implementation, not just technological novelty.

Fourth, inclusion must be designed into the system from the start. Women, underrepresented communities, workers outside major technology hubs, SMEs and professionals in roles most exposed to automation must not be treated as an afterthought. AI fluency cannot be a privilege for the few. It must become a capability distributed across the economy.

This is the human-capital layer of AI leadership that has been missing from too many AI strategies. At CFTE, our work has shown that responsible AI adoption is not a one-off training exercise. It is institutional readiness built as a system: defined through proficiency frameworks, delivered through role-based pathways tied to real workflows and measured through capability diagnostics.

Technology will continue to advance. Models will become more powerful, cheaper and more autonomous. But the countries that lead will not simply be those that move fastest. They will be those that build the deepest capacity to adapt, govern and create value responsibly.

For the UK, the real opportunity is not only to be a global AI innovator. It is to become a global model for responsible AI implementation.

That means recognising capability as infrastructure. It means treating AI fluency as seriously as compute, data and regulation. And it means ensuring that the people who power the economy are not passive recipients of technological change, but active shapers of it.

The future of agentic AI will not be determined by technology alone. It will be determined by people. The choice for the UK is whether we shape this shift, or are shaped by it.

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